Demographics

Demographic numbers to put in tables or text.

Everyone

Demographics for all included participants.

Demographics
Summary
N Age (years) Education (years) Sex (M/F/O) EHI
844 29.08 (6.03) 14.38 (2.48) 441/391/12 5.09 (79.37)


Race n
White 606
Black or African American 82
Multiple 76
Asian 69
American Indian or Alaska Native 5
Native Hawaiian or Other Pacific Islander 3
Other 3


Hispanic ethnicity n
No 744
Yes 100


By handedness group

Demographics for included participants, by handedness group (EHI bins).

Handedness N Age (years) Education (years) Sex (M/F/O) EHI
Left 331 28.84 (6.1) 14.45 (2.39) 170/157/4 -81.61 (19.27)
Mixed 135 28.83 (6.17) 14.58 (2.6) 77/56/2 -8.89 (26.49)
Right 378 29.38 (5.93) 14.24 (2.5) 194/178/6 86.01 (16.61)
Left: (EHI <= -40) | Mixed: (-40 < EHI < 40) | Right: (EHI >= 40)


EHI distribution









Preregistered 3-way (RT, bins)

Show interaction at each level: 8 boxes, 4 boxes, 2 boxes.

Dots show per-subject means, error bars show SEM.

TODO?. Show model-estimated mean & CI, instead of per-subject.


Zoom: EHI +/- 100

Show 3-way (categorical) for strong left vs. right handers. Error bars show SEM.

Zoom: Shape

Show 3-way (categorical) for strong left vs. right handers, for squares only (and/or squares vs circles). Error bars show SEM.

3-way (bins), model estimates

Diamonds and lineranges show mixed-effects model point estimates and 95% CI.





Preregistered 3-way (RT, continuous)

Subject-level means and SEM

Rejects

Dots only (no lines)

A dot for every subject

I think the dot for every subject is not very useful! I think the summary stats for LVF Global bias (top graph) help show the effect – the group of left handers on the far left really pops out. Coloring by categorical group could make it even clearer. And, it could be nice to superimpose a trend line (from the subject level data pictured?)

It also could be worth visualizing the summary stats from the model in this way, with the point estimates from the full mixed model.

Discussion

Questions for discussion

  • Should we show SEM/CI from the subject-level data, or from the full mixed model?
  • For the categorical plots, would it be more compelling and clear to visualize summary stats only, leaving out the subject-level dots? I like to “show all the data”, but in this case, the wide range makes the patterns too hard to see.
  • For the categorical plots, would it be more compelling and clear to visualize summary stats from the model, instead of/in addition to the subject-level data and subject level summary stats (which aren’t the estimates we will discuss in Results)?
  • Is it enough to show the “tier one” figures listed below? Should we show any figures illustrating zoom analyses, and the continuous analysis?
  • Which figures show the interaction most clearly? Could we just show the 4-box plot?
  • Is it worth showing the 8-box data for the main analysis? I think it would be nice to show this more “raw” data, to help people understand the design.
  • Any feedback on the design of the graphs?
  • Should we show the accuracy (null) results?

Proposal

Tier one

  1. EHI
  2. (a, b, c) Main result: Compound figure showing RT interaction (1-box, 4-box, 8-box) in full sample.

Tier two (could leave out if we need the space)

  1. (a, b) Zoom: compound figure showing effect of shape, within strong lefties (1-box, 4-box). This shows the stro
  2. Scatterplot-like figure showing RT interaction (continuous) [If convincing]

Brainstorm - all figures I think might be worth showing

  • EHI
  • (a, b, c) Main result: Compound figure showing RT interaction (1-box, 4-box, 8-box) in full sample.
  • (a, b) Zoom: Compound figure showing RT interaction (1-box, 4-box) in strong handers, or strong handers with squares only.
  • (a, b) Zoom: show effect of shape (1-box, 4-box), either in strong handers only (revealing largest effect size, and also illustrating the effect of shape), or in the full sample (focusing on the effect of shape itself)
  • Scatterplot-like figure showing RT interaction (continuous)